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Sep 17, 2020 · We show that this task can be accomplished by applying a Johnson-Lindenstrauss embedding and subsequently binarizing each vector by comparing ...
We consider the problem of encoding a set of vectors into a minimal number of bits while preserving information on their Euclidean geometry.
Sep 17, 2020 · We show that this task can be accomplished by applying a Johnson-. Lindenstrauss embedding and subsequently binarizing each vector by compar-.
Abstract. We consider the problem of encoding a set of vectors into a minimal number of bits while preserving information on their Euclidean geometry.
Abstract. Binary embedding is a nonlinear dimension re- duction methodology where high dimensional data are embedded into the Hamming cube while.
Sep 7, 2024 · We consider the problem of encoding a set of vectors into a minimal number of bits while preserving information on their Euclidean geometry.
We prove an extension of the Johnson-Lindenstrauss lemma (Sivaku- mar, 2002) for general pseudo-random structured projec- tions followed by nonlinear mappings.
Matrices with the restricted isometry property and with randomized column signs provide optimal Johnson–Lindenstrauss embeddings up to logarithmic factors in N.
We consider the hashing mechanism for constructing binary embeddings, that involves pseudo-random projections followed by nonlinear (sign function) mappings.
Abstract. Binary embedding is a nonlinear dimension reduction methodology where high dimensional data are embedded into the Hamming cube while preserving ...